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Classification and Regression Trees
This chapter discusses tree classification in the context of medicine, where right Sized Trees and Honest Estimates are considered and Bayes Rules and Partitions are used as guides to optimal pruning.
Greedy function approximation: A gradient boosting machine.
Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering.
Regularization Paths for Generalized Linear Models via Coordinate Descent.
In comparative timings, the new algorithms are considerably faster than competing methods and can handle large problems and can also deal efficiently with sparse features.
Sparse inverse covariance estimation with the graphical lasso.
Using a coordinate descent procedure for the lasso, a simple algorithm is developed that solves a 1000-node problem in at most a minute and is 30-4000 times faster than competing methods.
Stochastic gradient boosting
Gradient boosting constructs additive regression models by sequentially fitting a simple parameterized function (base learner) to current "pseudo'-residuals by least squares at each iteration. The
Discussion of the Paper \additive Logistic Regression: a Statistical View of Boosting" By
The main and important contribution of this paper is in establishing a connection between boosting, a newcomer to the statistics scene, and additive models. One of the main properties of boosting
Special Invited Paper-Additive logistic regression: A statistical view of boosting
Boosting is one of the most important recent developments in classification methodology. Boosting works by sequentially applying a classification algorithm to reweighted versions of the training data
Regularized Discriminant Analysis
Abstract Linear and quadratic discriminant analysis are considered in the small-sample, high-dimensional setting. Alternatives to the usual maximum likelihood (plug-in) estimates for the covariance